Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 6; pp. 1263 - 1275
• Main Authors: Zhu, Xiaofeng, Li, Xuelong, Zhang, Shichao, Ju, Chunhua, Wu, Xindong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Coding; Computational modeling; Data models; Design optimization; Dimensionality reduction; Embedding; Encoding; Graphical representations; Learning systems; Linear programming; manifold learning; Manifolds; Objective function; Outliers (statistics); regression; Robustness; Robustness (mathematics); sparse coding; State of the art

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods an...